How to use math to improve performance and predict outcomes in professional sports
Mathletics reveals the mathematical methods top coaches and managers use to evaluate players and improve team performance, and gives math enthusiasts the practical skills they need to enhance their understanding and enjoyment of their favorite sports―and maybe even gain the outside edge to winning bets. This second edition features new data, new players and teams, and new chapters on soccer, e-sports, golf, volleyball, gambling Calcuttas, analysis of camera data, Bayesian inference, ridge regression, and other statistical techniques. After reading Mathletics, you will understand why baseball teams should almost never bunt; why football overtime systems are unfair; why points, rebounds, and assists aren’t enough to determine who’s the NBA’s best player; and more.
Author(s): Wayne L. Winston, Scott Nestler, Konstantinos Pelechrinis
Edition: 2
Publisher: Princeton University Press
Year: 2022
Language: English
Commentary: Vector PDF
Pages: 608
City: Princeton, NJ
Tags: Data Analysis; Statistics; Probability Theory; Sport
Cover
Contents
Preface
Acknowledgments
Abbreviations
PART I. BASEBALL
1. Baseball’s Pythagorean Theorem
2. Who Had a Better Year: Mike Trout or Kris Bryant?
3. Evaluating Hitters by Linear Weights
4. Evaluating Hitters by Monte Carlo Simulation
5. Evaluating Baseball Pitchers, Forecasting Future Pitcher Performance, and an Introduction to Statcast
6. Baseball Decision Making
7. Evaluating Fielders
8. Win Probability Added (WPA)
9. Wins Above Replacement (WAR) and Player Salaries
10. Park Factors
11. Streakiness in Sports
12. The Platoon Effect
13. Was Tony Perez a Great Clutch Hitter?
14. Pitch Count, Pitcher Effectiveness, and PITCHf/x Data
15. Would Ted Williams Hit .406 today?
16. Was Joe DiMaggio’s 56-Game Hitting Streak the Greatest Sports Record of All Time?
17. Projecting Major League Performance
PART II. FOOTBALL
18. What Makes NFL Teams Win?
19. Who’s Better: Brady or Rodgers?
20. Football States and Values
21. Football Decision Making 101
22. If Passing Is Better than Running, Why Don’t Teams Always Pass?
23. Should We Go for a One-Point or a Two-Point Conversion?
24. To Give Up the Ball Is Better than to Receive: The Case of College Football Overtime
25. Has the NFL Finally Gotten the OT Rules Right?
26. How Valuable Are NFL Draft Picks?
27. Player Tracking Data in the NFL
PART. III. BASKET BALL
28. Basketball Statistics 101: The Four Factor Model
29. Linear Weights for Evaluating NBA Players
30. Adjusted +/- Player Ratings
31. ESPN RPM and FiveThirtyEight RAPTOR Ratings
32. NBA Lineup Analysis
33. Analyzing Team and Individual Matchups
34. NBA Salaries and the Value of a Draft Pick
35. Are NBA Officials Prejudiced?
36. Pick-n-Rolling to Win, the Death of Post Ups and Isos
37. SportVU, Second Spectrum, and the Spatial Basketball Data Revolution
38. In-Game Basketball Decision Making
PART IV. OTHER SPORTS
39. Soccer Analytics
40. Hockey Analytics
41. Volleyball Analytics
42. Golf Analytics
43. Analytics and Cyber Athletes: The Era of e-Sports
PART V. SPORTS GAMBLING
44. Sports Gambling 101
45. Freakonomics Meets the Bookmaker
46. Rating Sports Teams
47. From Point Ratings to Probabilities
48. The NCAA Evaluation Tool (NET)
49. Optimal Money Management: The Kelley Growth Criterion
50. Calcuttas
PART VI. METHODS AND MISCELLANEOUS
51. How to Work with Data Sources: Collecting and Visualizing Data
52. Assessing Players with Limited Data: The Bayesian Approach
53. Finding Latent Patterns through Matrix Factorization
54. Network Analysis in Sports
55. Elo Ratings
56. Comparing Players from Different Eras
57. Does Fatigue Make Cowards of Us All? The Case of NBA Back-to-Back Games and NFL Bye Weeks
58. The College Football Playoff
59. Quantifying Sports Collapses
60. Daily Fantasy Sports
Bibliography
Index
